full-rank

FuRA introduces a full-rank parameter-efficient fine-tuning method using spectral preconditioning via block tensor-train decomposition, achieving higher accuracy than full fine-tuning with LoRA-level efficiency. It outperforms LoRA and full FT on LLM and VLM tasks.